Experiencing climate change: revisiting the role of local
weather in affecting climate change awareness
and related policy preferences
#The Author(s) 2021
Over the last few years, climate change has risen to the top of the agenda in many
Western democracies, backed by a growing share of voters supporting climate protection
policies. To understand how and why these changes came about, we revisit the question
whether personal experiences with increasingly unusual local weather conditions affect
people’s beliefs about climate change and their related attitudes. We first take a closer
look at the theoretical underpinnings and extend the theoretical argument to account for
the differential impact of different weather phenomena, as well as the role of prior beliefs
and individual reference frames. Applying mixed-effects regressions to a novel dataset
combining individual-level multi-wave panel survey data from up to 18,010 German
voters collected from 2016 to 2019 with weather data from 514 weather stations, we show
that personally experiencing unusual or extreme local weather did not shape people’s
awareness of climate change as a political problem or their climate policy preferences in a
sustained manner. Even among people who may be considered most likely to exhibit such
effects, we did not detect them. Moreover, we demonstrate that the common modeling
strategy of combining fixed-effects regression with clustered standard errors leads to
severely reduced standard errors and substantively different results. We conclude that it
cannot be taken for granted that personally experiencing extreme weather phenomena
makes a difference in perceptions of climate change and related policy preferences.
Keywords Climatechange .Policy pref erences .Experientiallearning.Mixed-effectsregression.
Chair for Political Science, Political Psychology, University of Mannheim, A5, 6, 68131 Mannheim,
Received: 30 March 2021 / Accepted: 14 July 2021/ Published online: 6 August 2021
Climatic Change (2021) 167: 31
Over the last few years, climate change has risen to the top of the agenda in many Western
democracies. From the European Green Deal to President Biden’s Clean Energy Revolution
for the USA, longstanding commitments to fight climate change are tentatively being put into
action, in accordance with a growing share of the electorate supporting such action (e.g.,
Eurobarometer 2019; Poortinga et al. 2018; Flynn et al. 2021). People’s increasing awareness
of climate change as a problem and their changing preferences for related policies raise the
question how and why these changes came about. A burgeoning literature linking climate
change attitudes to personally experienced weather events suggests that an increase in unusual
weather conditions such as heatwaves, droughts, severe storms, and floods in recent years may
have made climate change more tangible, as people now experience its consequences firsthand
(e.g., Egan and Mullin 2012;Krosnicketal.2006;Lietal.2011; Myers et al. 2013). Drawing
on theories of risk perception, it is argued that personal experience, more than even large
amounts of cognitive information, offers an easily available cue attesting to the immediacy of
the risk and, hence, the personal relevance of an issue (e.g., Howe et al. 2014). The empirical
evidence largely supports this linkage, although the effects of objectively measured weather
conditions tend to be small and ephemeral compared to other predictors of climate change
beliefs and attitudes, including the effects of self-reported experiences with unusual or extreme
weather (cf. Hornsey et al. 2016; Marquart-Pyatt et al. 2014; McCright and Dunlap 2011).
These findings could reflect the methodological challenges involved in matching weather and
survey data but may also point to more fundamental theoretical issues. In the former case,
research designs using more fine-grained data may reveal more substantial effects. In the latter
case, there may be sound theoretical reasons not to expect large or lasting effects of personal
experiences on climate change attitudes.
We revisit the question whether personally experienced variations in local weather condi-
tions alter people’s beliefs about climate change and their related attitudes, taking a closer look
at the theoretical underpinnings and drawing on a new dataset that allows us to address some
of the challenges associated with objective weather data. The theoretical argument that links
personal experience to an increased sense of risk that may result in an according change in
individual beliefs and attitudes is sound and empirically supported. However, in the case of
climate change, this argument needs to be elaborated to account for the differential impact of
different weather phenomena, as well as the role of prior beliefs and individual reference
frames in shaping this impact. Furthermore, given the range of available climate policy
options, the link between personal experience and attitudes is based on additional assumptions
that need to be specified. We hence extend the theoretical argument to account for the specific
features of the case.
To test whether and how objective variations in local weather conditions affect people’s
awareness of climate change as a problem and their related attitudes, we combine individual-
level panel data from up to 18,010 respondents with objective weather data from 514 weather
stations currently in use across Germany, which was first strongly affected by climate change
in 2018 (Rueter 2019). The panel structure of the data enables us to trace intra-individual
changes in the salience of climate change and related policy preferences over time while
minimizing unobserved heterogeneity, thus facilitating causal inference (Howe et al. 2019;
Palm et al. 2017;Shaoetal.2014). The high geospatial resolution of the weather data allows
us to match individual attitudes and weather conditions at the station level, maximizing the
likelihood that people actually experienced the weather attributed to their area and thereby
Climatic Change (2021) 167: 31
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eliminating a great number of confounding factors (cf. Konisky et al. 2016). Both strategies
have been employed in previous research to overcome challenges to causal inferences, but to
date, only oneother study has combined these strengths in the same research design (Ripberger
et al. 2017).
When looking for factors that shape people’s beliefs about climate change and attitudes
towards related policies, local weather appears to be a natural candidate. People experience
local weather directly, suggesting that this information may be salient in forming climate
change beliefs and related policy attitudes. In line with this personal experience model,
numerous studies have found a small but largely consistent influence of unusual variations
in local weather on climate change beliefs, levels of concern, and attitudes towards related
policies. Real and perceived deviations from long-term average temperatures (Akerlof et al.
2013;Brooksetal.2014; Capstick and Pidgeon 2014; Egan and Mullin 2012;Lietal.2011;
Myers et al. 2013), as well as extreme weather events such as severe droughts, storms, and
flooding (Borick and Rabe 2014;Krosnicketal.2006; Lujala et al. 2015; Reser et al. 2012;
Spence et al. 2011;Tayloretal.2014;vanderLinden2015; but see Brulle et al. 2012), have
all been linked to an increased belief in and concern about climate change. Individuals who
have experienced extreme weather events have also been shown to become more supportive of
climate protection policies (Rudman et al. 2013). The extensive literature on risk perceptions
suggests that personal experience serves as an easily available cue for people’s risk assess-
ments, making abstract risks such as climate change more tangible (Kahneman et al. 1982;
Keller et al. 2006;KuchlerandZafar2019; Reser et al. 2014;Weber2006). People generally
tend to underestimate risks, yet they routinely overestimate risks they have previously
experienced, and this increased sense of personal risk motivates behavioral changes (Fazio
and Zanna 1981;Howeetal.2014, p. 382; for a comprehensive discussion see Whitmarsh
2008). Hence, unusual variations in local weather may serve as a simple cue attesting to the
personal relevance of climate change.
However, a closer look at this link reveals that the theoretical connection between people’s
personal experiences with local weather conditions and their beliefs about climate change and
attitudes towards related policies may not be as straightforward as it seems. It requires people
to respond to certain local weather phenomena by changing their beliefs and attitudes in a
specific direction, building on assumptions that deserve closer attention. To begin with,
citizens’political perceptions and attitudes are often affected by pre-existing views and
predispositions leading to biased information processing (e.g., Kunda 1990; Lodge and
Taber 2013;Lordetal.1979). For example, partisan attachments have been demonstrated to
shape people’s perceptions and make them less responsive to real-world events than one may
expect (e.g., Devine-Wright et al. 2015; Evans and Pickup 2010; Fielding and Hornsey 2016;
Hoffarth and Hodson 2016;butseeZiegler2017). Long-standing political preferences may
thus attenuate the responsiveness of citizens’attitudes towards climate change and related
policies to local weather phenomena. In a similar vein, existing beliefs about climate change
likely affect the probability that local weather conditions are perceived as indicative of global
climate trends. For climate change skeptics, defensive motivated reasoning can be expected to
prevent attitudinal changes in response to local weather phenomena (e.g., Kunda 1990;Lodge
and Taber 2013;Lordetal.1979). Put differently, it is an open question whether even extreme
Climatic Change (2021) 167: 31 Page 3 of20 31
local weather phenomena are sufficiently significant for many people to overcome the impact
of their predispositions.
As a consequence, unusual local weather conditions may not
significantly influence people’s climate change concern or their related attitudes (H1).
Even if local weather conditions did affect people’s climate change beliefs and attitudes,
this influence cannot be expected to be universal. Looking at the dependent variables that have
been considered in prior research, they appear to differ in their substantive closeness to local
weather conditions. Building on the far-ranging scientific consensus (Field et al. 2012),
extreme local weather conditions are linked to changes in the global climate and may thus
be considered indicative of climate change. Accordingly, extreme local weather conditions
likely affect people’s beliefs about the existence of climate change and its perceived personal
importance. It is quite a different matter, however, to link such experiences to support for
specific policies. In the case under study, people may agree on the existence of climate change
and even perceive it as an important problem, yet still disagree about the best way to address it.
Depending on their beliefs and values, some people will plead for climate protection, whereas
others prefer adaptation (Tvinnereim et al. 2017; also see Alló and Loureiro 2014;Schwirplies
2018). From this follows the expectation that attitudes towards climate-related policies are
less likely to exhibit systematic effects of local weather conditions than beliefs about the
existence of climate change and its personal importance (H2).
Moreover, weather is a multidimensional concept that includes a variety of phenomena
such as temperature, pressure, and precipitation. Given that climate change is most often
referred to in terms of rising temperatures (consider the term “global warming”)orinthe
context of extreme weather events such as hurricanes and floods, certain weather phenomena
such as heat and drought may be easier to link to climate change than others such as unusually
intense snow fall and cold, because their connection to a trend characterized primarily by
higher average temperatures is less obvious (van der Linden 2014;Weber2010; also see Lang
2014). Thus, local weather conditions related to rising temperatures and weather extremes
are expected to be more likely to influence people’s climate change concern and their support
for climate protection policies than other weather events (H3).
The global nature of climate change also raises the question whether unusual variations in
local weather will be considered informative given that most local weather conditions,
however unusual for the moderate Western European climate, will seem negligible compared
to the global consequences of climate change, which tend to receive extensive media attention.
As a result, we expect that local weather conditions are more likely to influence people who
have a strong focus on local conditions (H4a), motivated by, for example, genuine parochi-
alism (e.g., Inglehart 1970) or self-interest. In other words, some people may primarily care for
their local surroundings, whereas others may not generally ignore developments beyond their
local region but could be particularly responsive to local weather conditions because of their
self-interest. Consider, for instance, farmers and elderly people, who have a strong interest in
local weather conditions and may thus be particularly responsive to them (Filiberto et al.
2009). However, as argued above, strong attachment to the local community may prevent
people from linking local conditions to global trends, leading us to expect that people who
have a strong focus on local conditions are less likely to exhibit changes in their beliefs about
the existence of climate change (H4b).
In addition, we do not know how quickly people’s expectations adapt to recent experiences. At the extreme,
expectations may change quickly, making even extreme weather events seem ordinary and thus no longer
Climatic Change (2021) 167: 31
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With regard to policy attitudes, the considerations about predispositions presented above
suggest that local weather conditions may primarily influence people who already accept the
existence of climate change, yet do not endorse a certain set of policies. For those fully aware
of the serious global ramifications of climate change, even extreme local weather conditions
will probably not be alarming enough to influence their positions on climate policy. However,
for people who accept the existence of climate change but do not perceive its consequences as
a personal risk, unusual variations in local weather may make climate protection seem more
urgent and thus prompt changes in their political attitudes. Such changes may follow from an
improved understanding of the linkages between local weather variations and global climate
change, or from a simple attribution error enabling people to link (perceived) local weather
extremes to support for local policy measures while disregarding the complexity of this global
phenomenon (Gilbert and Malone 1995; Jones and Harris 1967;Lordetal.1979;Ross1977).
Despite their larger substantive distance to local weather phenomena, we thus hypothesize that
policy-related attitudes are more likely to respond to these conditions for people who already
accept the existence of climate change (H5).
Taken together, the analysis suggests that effects of local weather conditions on beliefs
about the existence of climate change, the awareness of climate change as a problem, and
support for climate protection policies cannot be taken for granted. If anything, local weather
conditions commonly associated with climate change should influence the problem awareness
and, less so, the climate policy preferences of people with a strong interest in local weather
conditions who already accept the existence of climate change.
To understand the impact of personally experienced weather events, we study changes in
Germans’awareness of climate change as a problem and their climate policy preferences
between 2016 and 2019. Though not the most intuitive choice at the first glance, this case
offers two distinct advantages in testing the independent effect of local weather conditions.
First, although an overwhelming majority of Germans accept the existence of climate change
(ESS Round 8 2016), assessments of the risks associated with climate change and positions on
climate protection policies vary widely (Roßteutscher et al. 2018). Moving beyond the often
studied influence of weather events on beliefs about the existence of climate change allows us
to capture subtler variations in people’s awareness of climate change as a problem and their
corresponding attitudes, which are more closely related to decisions about concrete policies
than broader beliefs about the existence of climate change.
Second, although Germany has a relatively uniform cold-temperate climate (Peel et al.
2007), the past years have seen an ever increasing number of weather extremes (Rueter 2019).
Importantly, the impact of these events was not evenly distributed across the country but
affected some regions, counties, and sometimes even cities more than others. Temperatures
climbed to record highs in 2018 and 2019, causing or exacerbating droughts across the country
(Friedrich and Kaspar 2019; Imbery et al. 2019; Meinert et al. 2019). With forest fires
requiring evacuation in some parts and water rationing in others, a phenomenon that mainly
concerned farmers for a long time suddenly affected people’s everyday lives (Handelsblatt
2019;Nicolai2018;ZeitOnline2018). At the same time, storms were frequent and unusually
destructive, interrupting travel routes and resulting in considerable property damages
(Deutsche Welle 2018; Rueter 2019). In some areas, local flooding and heavy snowfalls put
Climatic Change (2021) 167: 31 Page 5 of20 31
people out of their homes (DWD 2018;WAZ2019). While many of these events were closely
followed by the media and may thus have influenced attitudes towards climate change on the
aggregate level, their impact was only personally experienced in relatively small areas. Hence,
the studied case is well suited to examine the independent impact of personally experienced
weather conditions on climate change perceptions and related attitudes.
4 Data and methods
To explore the impact of objective local weather on people’s climate change attitudes, we
combine panel survey data from the 2017 Campaign Panel of the German Longitudinal
Election Study (Roßteutscher et al. 2018) with objective weather data provided by the German
Meteorological Service (DWD Climate Data Center (CDC) 2018; Ziese et al. 2013) and the
European Centre for Medium-Range Weather Forecasts (ECMWF). This allows us to match
the climate change attitudes of up to 18,010 respondents to objective weather data from 514
weather stations currently in use across Germany. By combining the most fine-grained
geospatial resolution to date with the possibility to trace intra-individual changes in the policy
preferences of respondents who experienced different weather conditions over time, the used
data offer an advantage over most prior studies that utilize these strengths in isolation (for an
exception, see Ripberger et al. 2017). Figure 1depicts the collection dates of the twelve waves
of the GLES Campaign Panel that were used in the analyses against the backdrop of
aggregated levels of temperatures and drought over the same period (additional indicators
are included in Appendix 1). The GLES Campaign Panel 2017 draws its respondents from an
online access panel. Although quotas are used to ensure that the sample is representative with
regard to respondents’gender, age, and education levels, self-selection and panel attrition
introduce biases not found in random probability samples. For instance, respondents tend to be
slightly more involved in politics and more partisan than the larger population.
On a scale from 0 to 1, respondents’political interest averages at 0.59 in the Campaign Panel, compared to 0.56
in the Cumulated Pre- and Post-election Cross Section (GLES 2019), which uses a random probability sample.
Similarly, the share of party identifiers is 77.9% in the Campaign Panel, compared to 76.3% in the cross section.
Fig. 1 Timeline of GLES Campaign Panel survey waves and average heat-related weather conditions. GLES
Campaign Panel survey waves were collected from 10/06–11/10/2016 (wave 1), 02/16–03/03/2017 (wave 2), 05/
11–05/23/2017 (wave 3), 07/06–07/17/2017 (wave 4), 08/17–08/28/2017 (wave 5), 09/04–09/13/2017 (wave 6),
09/18–09/23/2017 (wave 7), 09/27–10/09/2017 (wave 8), 03/15–03/26/2018 (wave 9), 11/06–11/20/2018 (wave
10), 05/28–06/12/2019 (wave 11), and 11/05–11/19/2019 (wave 12); depicted are daily temperatures and
monthly GPCC-DI scores (Source: DWD) averaged across respondents
Climatic Change (2021) 167: 31
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because we are interested in intra-individual changes rather than absolute levels, these biases
would threaten the validity of our results only if the affected variables moderated the influence
of local weather conditions on respondents’climate change concern and related attitudes.
Since more politically involved respondents may have more stable attitudes, we compared the
effects for politically less involved respondents to the effects observed in the full sample (see
Appendix 2), but the results remain substantively unchanged.
To measure unusual variations in local weather, we constructed daily binary indicators of
absolute and relative heat, oppressiveness, absolute and relative cold, (thunder-)storms, heavy
rain- and snowfall, and drought.
We relied on the DWD’s extreme weather classification
scheme to identify weather conditions that would prompt extreme weather alerts and should
therefore be unusual enough to capture respondents’attention (see Appendix 4for detailed
coding information). Although studies relying on self-reported exposure to extreme or unusual
weather routinely find larger effects than studies based on objective weather conditions
(Akerlof et al. 2013;Brooksetal.2014; Hornsey et al. 2016;Tayloretal.2014), these studies
are not well-suited for causal inference because item priming effects cannot be precluded and
perceptions of objective weather conditions are affected by existing climate change beliefs
(Howe and Leiserowitz 2013; Myers et al. 2013). Moreover, the perception of unusual or
extreme weather is an important step in the causal chain between objective weather conditions
and climate change attitudes, which is omitted in these studies, increasing the risk for
overestimating effects. To avoid the challenges associated with self-reports and to maximize
the likelihood that respondents actually experienced objective weather conditions recorded for
their area, we matched the postal codes of up to 18,010 respondents who participated in one or
more waves of the 2017 GLES Campaign Panel (Roßteutscher et al. 2018) to one of 514
German weather stations operated by the DWD. To this end, we minimized the Euclidian
distance between the coordinates marking the center of a postal area and the coordinates of the
weather stations. We added indices for thunderstorms (Hersbach et al. 2018) and droughts
(Ziese et al. 2014) available at different grid resolutions following the same procedure.
These regional clusters are extremely small compared to the commonly used Climate
Extremes Index (CEI), which measures weather extremes for nine interstate regions across
the USA (e.g., Konisky et al. 2016; Marquart-Pyatt et al. 2014). With a mean distance of 0.1
degrees in latitude/longitude (approximately 7 to 11 km) compared to 0.43 degrees in latitude/
longitude (approximately 40 to 48 km), the distances between weather stations and postal areas
are also considerably smaller than the distances resulting from matching US weather stations
and Zip Code Tabulation Areas (ZCTA; Egan and Mullin 2012).
Figure 2shows the
distribution of weather stations (green markers) and postal areas using the example of ten
weather stations located in urban centers (first and third column) and rural areas (second and
fourth column) across Germany. The gridlines indicate degree minutes, roughly 1.2 km in
latitude and 1.9 km in longitude, illustrating the different shapes and sizes the areas associated
with the weather stations can take. As weather data is available only at the station level, the
same value is recorded for all postal codes linked to the station. The background coloring in
Fig. 2indicates the mean daily temperature on June 6, 2018, which averaged 19.5 °C and was
thus 4.1 °C higher than the long-time normal temperature. Importantly, the mean temperature
Given the rarity of veryextreme weather events, continuous measures of weather extremity do not offer enough
variation to obtain reliable estimates, but robustness checks with two survey waves show no substantial
differences in the results in cases where the models converge (see Appendix 3).
Even the maximum distance of 0.39 degrees in latitude/longitude (approximately 28 to 43 km) is still smaller
than the mean distance between US weather stations and ZCTAs.
Climatic Change (2021) 167: 31 Page 7 of20 31
varied considerably across weather stations, ranging between 4.3 and 24.0 °C (between
17.2 °C at station 2483 and 23.4 °C at station 1424 for the example in Fig. 2), indicating
greatly different local weather experiences for respondents from different neighborhoods.
To measure respondents’climate change perceptions and attitudes, we relied on three
different items recording whether climate change is considered the most important problem
facing the country, whether fighting climate change should be prioritized over economic
growth, and whether cars with combustion engines should be allowed to be registered after
2030 (see Appendix 5for question wording and coding). The first item is binary and captures
Fig. 2 Mean daily temperature for different weather stations across Germany. Depicted are weather stations
(green) and the borders of the corresponding postal areas across Germany; the gridlines indicate degree minutes
in latitude/longitude; the background coloring indicates the mean daily temperature (Source: DWD)
Climatic Change (2021) 167: 31
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respondents’general awareness of climate change as a problem,
whereas the second item
queries respondents’priorities when faced with conflicting policy aims on a 7-point scale. The
third item records respondents’support for a specific measure to fight climate change on a 5-
point scale and may thus be contested among respondents who support climate protection
measures in general. Hence, the three measures capture different levels of commitment to
fighting climate change, from problem awareness to specific policy measures. Unfortunately,
only the last wave of the panel included an item asking respondents about the existence of
climate change, preventing its inclusion in the analysis. However, given that over 90% of
Germans accept the existence of climate change (ESS Round 8 2016), the greater variation in
the three available items allows us to capture more nuanced shifts in respondents’problem
awareness, priorities, and policy preferences.
Table 1displays descriptive statistics for each item and details the waves in which they
were queried. Since the survey waves were timed to match the election cycle and the items
were not included in every wave, the intervals between interviews vary considerably from a
week to 6 months, raising the question how people processed the experienced local weather
conditions. Irrespective of preferences for on-line or memory-based models of information
processing (Kim and Garrett 2012; Lodge et al. 1995), longer intervals could make identifi-
cation more difficult because respondents likely perceived more potentially relevant stimuli
between interviews. We can thus leverage the different interval lengths to gauge the longevity
of attitude changes associated with local weather conditions.
Whereas most previous studies working with fine-grained weather data relied on cross-
sectional surveys and thus examined inter-individual differences between the climate change
beliefs of respondents who experienced different weather conditions, we used panel data to
explore intra-individual differences over time. Specifically, the panel data allowed us to test
whether unusual variations in local weather between two survey waves elicited changes in
respondents’climate change attitudes relative to their previous positions. While cross-sectional
designs can only control for a finite number of individual- and regional-level confounders, the
panel design minimizes unobserved heterogeneity (cf. Howe et al. 2019). To confound the
results, omitted variables would need to emerge between survey waves, eliminating the
influence of long-term and asynchronous factors. In combination with the high spatial
resolution of the data, confounding factors would additionally need to mirror the spatial
distribution of the weather stations. This constellation rules out most of the factors that have
been shown to influence people’s climate change attitudes because their spatial distribution
differs considerably from the distribution of weather stations. This includes coverage by
This item prompts respondents to name the one most important problem facing the country (see Appendix 5for
the precise wording), but many respondents still name more than one problem. Since any mention in response to
the question is obviously salient in the respondents’minds, climate change was recorded as the most important
problem even if it was mentioned alongside other problems.
Table 1 Dependent variables
Mean Mdn SD Min Max Waves
Problem awareness 0.03 0 0.17 0 1 1–12
Policy priority 0.64 0.5 0.26 0 1 1, 2, 4, 7, 8, 10–12
Solution preference 0.40 0 0.33 0 1 4–8, 11–12
Climatic Change (2021) 167: 31 Page 9 of20 31
national, regional, and most local media, as well as election campaign messages that may be of
special concern for a survey instrument timed to represent the election cycle.
Although the panel design increases our confidence that we capture the impact of local
weather, we cannot preclude the possibility that local weather conditions affect respondents’
climate change attitudes in conjunction with other factors. For instance, people may experience
high temperatures but only perceive this experience as unusual and associate it with climate
change because the media tell them that climate change has brought the second-hottest summer
in two centuries. In this case, the personal experience and the media report would jointly
change people’s climate policy preferences. We thus argue only that the personal experience
played an integral part in the change, not that it is solely responsible. Similarly, people may
experience unusual variations in local weather vicariously through a personal conversation or a
local radio report rather than personally. Importantly, vicarious experiences will only be
captured if they relate to local weather conditions in the respondents’immediate environment
that could have been personally experienced; we thus remain agnostic about this question.
To analyze the influence of unusual variations in local weather, we first calculated changes
in respondents’climate change positions to obtain dynamic dependent variables. Accordingly,
problem_c indicates whether a respondent who did not name climate change as the most
important problem in the previous survey wave named it in the current wave, whereas
priority_c and solution_c capture changes in respondents’positions on these items. The
weather variables were recoded to reflect the number of days, or months in the case of
drought, for which unusual weather events were recorded in between the previous survey
wave and the day of the interview for the current survey wave. Hence, the values of these
variables differ across weather stations and survey days but are identical for respondents who
live near the same weather station and completed their interview on the same day.
To account for this three-level data structure, we estimated mixed-effects models with fixed
effects and random intercepts for the weather station and interview date. Considering that the
weather at different stations across Germany is not necessarily more similar on the same day
than on different days, the random intercept for the interview date is nested within the regional
cluster of weather stations.
Since we are analyzing intra-individual changes, individual-level
controls are not required.
Instead of using mixed-effects models, even sophisticated previous research has tended to
rely on adding fixed effects for temporal and regional clusters to standard regression models
and clustering the standard errors at the level where the weather variations occur (cf. Egan and
Mullin 2012), partly because meaningful mixed-effects estimates require very large samples.
However, models without random intercepts for clusters included as fixed effects effectively
ignore within-level interdependencies and may thus lead to seriously downward-biased stan-
dard errors (Schmidt-Catran and Fairbrother 2016). The inclusion of random intercepts
prevents this bias and can therefore be expected to result in more accurate estimations. To
explore the severity of the bias for models that rely on fixed effects with clustered standard
errors only, we re-ran all models with fixed effects for the weather station and interview date,
clustering the standard errors at the station level.
Finally, we explored potential heterogeneous effects for people who are routinely more or
less affected by local weather conditions. Unfortunately, the number of farmers in the sample
While crossed random intercepts control for any unexpected variation and are thus generally preferable to
nested random intercepts (Schmidt-Catran and Fairbrother 2016), the sample is too small to estimate meaningful
crossed random intercepts for all weather stations and interview dates.
Climatic Change (2021) 167: 31
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was too small to obtain meaningful estimates for this heavily affected subgroup; however,
older people also tend to be more immediately affected by extreme weather events. Hence, we
re-ran the models capturing changes from November 2018 to November 2019 for different age
groups to test whether people aged 60 and older were more likely to respond to local weather
conditions than younger respondents. To assess whether local weather conditions more
strongly affected people who are more locally minded, we re-ran the same models separately
for respondents who indicated that they were strongly or rather strongly attached to their
municipality and respondents who felt at most moderately attached.
In addition, we explored
whether respondents’level of education or gender influenced their propensity to link local
weather conditions to climate change and to adjust their related attitudes (see Appendix 5for
question wording and coding).
In principle, climate change skeptics have the largest potential for changing their climate-
related attitudes in response to local weather extremes, whereas people who accept the
existence of climate change likely already adjusted their attitudes and hence experience a
ceiling effect. Although we suspect that motivated reasoning prevented climate change
skeptics from realizing this potential, we empirically tested this assumption with data collected
in November 2019, when respondents were asked whether or not they believed that the climate
is changing. We re-ran the according models separately for climate change skeptics and
acceptors to capture potential differences between these groups.
To test whether weather conditions that either were or could have been personally experienced
affect people’s perceptions of climate change as a problem, their willingness to prioritize
climate protection over economic growth, and their preferences regarding climate protection
measures, specifically the registration of cars with combustion engines after 2030, we first
estimated mixed-effects models with fixed effects and random intercepts for the weather
station and interview date for all available survey waves.
The independent variables are the
indicators for absolute and relative heat, oppressiveness, absolute and relative cold, (thun-
der-)storms, heavy rain- and snowfall, and drought, measured between the survey wave in
which the dependent variable was collected and the previous wave. We estimate the likelihood
that respondents name climate change as the most important problem when it was not named
in the previous wave using logistic regressions (models A.1.) and changes in respondents’
policy priorities (models A.2.) and solution preferences (models A.3.) using linear regression.
For the latter models, positive coefficients indicate priority shifts in favor of climate protection
and not registering cars with combustion engines after 2030, respectively. To allow for easy
interpretation and comparison of the results from the 18 mixed-effects models and 19 fixed-
effects models with clustered standard errors, the results are depicted in specification curves,
which include point estimates with 95% confidence intervals for the ten independent weather
variables (listed on the left) and indicate the respective dependent variable, the modeling
We also re-ran the models only for those respondents who felt strongly attached to their municipality, with
substantively unchanged results.
Convergence was not achieved for problem awareness in waves 3 and 8, policy priorities in waves 7 and 8, and
solution preferences in wave 8 (wave 8 was the post-election wave, collected only days after the previous wave).
Climatic Change (2021) 167: 31 Page 11 of20 31
strategy, and the survey waves in which the data was collected in the bottom (Simonsohn et al.
Supporting the first hypothesis, the results displayed in Fig. 3show that local weather
conditions do not affect people’s awareness of climate change as a problem, their policy
priorities, or their solution preferences (see Appendix 7for the full regression results). Only 7
out of 176 coefficients achieve statistical significance (highlighted in red), which is less than
the nine coefficients that would be expected to be significant by chance alone. Moreover, the
significant coefficients do not follow any pattern that would indicate a systematic short- or
long-term influence of local weather conditions on either problem awareness, policy priorities,
or solution preferences. Contrary to our initial expectations, there is no evidence that problem
awareness is affected more than specific policy preferences, nor that weather phenomena
commonly associated with climate change influence people’s perceptions and attitudes more
strongly. Hence, the empirical evidence does not corroborate the second or third hypothesis.
The coefficients do not pass conventional levels of statistical significance across all waves,
indicating that local weather conditions had neither ephemeral nor lasting effects. In short,
even the unusual and relatively extreme local weather conditions observed during the data
collection period did not influence the climate-related attitudes of the population at large.
In a second step, we aimed to determine whether model specification matters for the results
and substantive conclusions. To this end, we re-estimated all models
using only the fixed
effects and standard errors clustered at the station level to assess how this common model
specification affects the results. Comparing the coefficients of the fixed-effects models (models
B) shown on the right-hand side of Fig. 3with the mixed-effects models on the left (models
A), we see that 52 out of 186 coefficients are statistically significant, some of them indicating
rather large effects. This equals roughly 28% of all coefficients, which is seven times as many
as with the mixed-effects models and considerably more than 5%, suggesting that the standard
errors are severely underestimated in models without random intercepts.
These results demonstrate that studies which fail to take within-level interdependencies into
account will routinely overestimate the influence of weather phenomena, especially when
using cross-sectional designs with a single dependent variable. In the large time series at hand,
it is easy to see that the significant coefficients do not follow any systematic pattern. However,
the number of significant coefficients suggests that every third or fourth cross-sectional
estimation may return a false positive. In the absence of additional estimates, this effect will
likely be interpreted at face value, resulting inmisleading conclusions about the impact of local
weather phenomena on people’s climate change beliefs and related attitudes.
Although local weather conditions do not influence people’s climate change beliefs and
related attitudes in the population at large, they may still be important predictors for the beliefs
and attitudes of people who, for whatever reason, are more attentive to their local weather. One
Fig. 3 Impact of local weather conditions on problem awareness, policy priorities, and solution preferences.
Depicted are coefficients for the ten weather indicators with 95% confidence intervals. The weather indicators are
listed on the left and ordered by model specification; i.e., coefficients from the same model are depicted below
each other. The legend on the bottom indicates the respective dependent variable (DV) for each column of
coefficients, whether mixed-effects (models A) or fixed-effects with clustered standard errors (models B) were
estimated, and whether logistic or linear regression was used (model). The data collection period is also reported
(wave). Significant effects are highlighted in red
The number of coefficients differs because, unlike the mixed-effects models, the fixed-effects models failed to
converge for problem awareness in wave 12 but converged for respondents’policy priorities and solution
preferences in wave 8.
Climatic Change (2021) 167: 31
Page 12 of20
Climatic Change (2021) 167: 31 Page 13 of20 31
segment of the population whose daily life is routinely more affected by local weather
conditions and for whom the local weather could thus have a more pronounced impact are
older people. This is not the case, as the models depicted on the left hand in Fig. 4show no
systematic pattern of effects, even for this likely case (see Appendix 8for the full regression
results). Another group whose climate-related attitudes may be more strongly affected by local
Fig. 4 Impact of local weather conditions on problem awareness, policy priorities, and solution preferences by
age group (blue markers) and municipal attachment (green markers) between Nov. 2018 and Nov. 2019.
Depicted are coefficients for the ten weather indicators with 95% confidenceintervals from mixed-effects models
only. See the caption of Fig. 3for further interpretative guidance
Climatic Change (2021) 167: 31
Page 14 of20
weather conditions are people who are more focused on their local surroundings, but the
models depicted on the right hand in Fig. 4also show no evidence of meaningful variation.
Hence, neither hypothesis 4a nor 4b is empirically supported by the results. Similarly,
respondents’gender and level of education do not seem to play a role (see Appendix 9).
In November 2019, respondents were also asked whether or not they believed that the
climate is changing, enabling us to explore the impact of existing predispositions. To reiterate,
climate change skeptics have the largest potential for changing their climate-related attitudes,
but motivated reasoning likely prevents them from associating variations in local weather with
climate change. On the other hand, people who accept the existence of climate change likely
already have corresponding priorities and preferences, leaving only those who believe that
climate change exists, but who had not previously considered climate change to be a personal
risk, with potential for change. In line with hypothesis 1 and contrary to hypothesis 5, the
subgroup analyses show no differences between climate change skeptics and acceptors, as
neither group’s problem awareness, policy priorities, or solution preferences are affected by
their local weather (see Appendix 10 for the regression results).
A growing literature implies that the increasingly tangible consequences of climate change
may heighten people’s personal risk perception and their willingness to support climate
protection policies. While there is evidence that experiencing unusual variations in local
weather conditions or extreme weather events increases the likelihood of accepting the
existence of climate change, the impact of personal experiences on attitudes that are more
closely related to political action has received less empirical scrutiny (for an exception, see
Rudman et al. 2013). We examined the influence of unusual and extreme local weather
conditions on people’s awareness of climate change as a (political) problem, their willingness
to prioritize climate protection over economic growth, and their position on banning combus-
tion engines from 2030 onwards. The results from panel survey data collected in Germany
show that the indicators for ten different local weather phenomena associated with climate
change were neither related to respondents’positions on concrete climate protection measures
nor to their policy priorities. Experiencing unusual weather conditions did not even raise the
likelihood that respondents perceived climate change as one of the most important problems
facing the country, and this pattern held across several subgroups theorized to be particularly
affected by local weather conditions. In short, personal experiences with unusual or extreme
weather may affect people’s belief in the existence of climate change, but this belief does not
seem to find expression in their policy positions.
This finding suggests some caution against the role of personal experiences in shaping
people’s views of climate change and related policies. Given similar findings on other, for
instance economic (e.g., Duch and Stevenson 2008), conditions and considering the large
number of assumptions underlying the linkage between personal experiences with unusual or
extreme weather events and people’s climate-related attitudes, this pattern is not entirely
surprising. It is beyond the scope of this paper to detail which factors contributed to the
unresponsiveness of people’s problem awareness and policy preferences to local weather
extremes. Thus, questions regarding, for instance, whether political predispositions biased
weather perceptions and their processing, or whether people were unable or unwilling to
connect personal experiences to their political views, remain unclear. Likewise, our analysis
Climatic Change (2021) 167: 31 Page 15 of20 31
does not preclude the possibility that, in line with the primacy of sociotropic perceptions,
weather phenomena at a different scale such as the national level affected citizens’views and
attitudes. Future research should therefore take a closer look at such alternative effects and the
mechanisms preventing personal weather phenomena from strongly affecting citizens’atti-
tudes towards climate change and related policies.
Like any empirical research, this analysis suffers from methodological limitations. Al-
though the intervals between the survey waves ranged between several days and up to 6
months, the shorter intervals were clustered around the federal election in September 2017 and
rather uneventful with regard to the weather. In consequence, our research design may have
failed to capture short-term effects of local weather events. While such ephemeral effects
would be of interest to better understand the psychological mechanisms underlying risk
assessments, they would also be unlikely to affect people’s policy choices in a sustained
manner and hence largely irrelevant for explaining climate-friendly policy outcomes.
Moreover, our results are bound to Germany from 2016 to 2019. The personal experience
hypothesis may have fared better in a period or region with more extreme weather events of
longer duration with more tangible personal consequences. Likewise, studying these effects in
an earlier period when climate change and related policies were rather novel and less
politicized phenomena may have resulted in a different picture. Although we included a range
of dependent variables to ensure that the results are not specific to one indicator, there may be
climate protection policies that are perceived as more closely linked to local weather and,
hence, potentially more susceptible to unusual variations in local weather conditions than our
dependent variables. We thus suggest additional research using similar research designs to
explore the generalizability of these findings.
Notwithstanding these limitations, our analysis suggests some caution against the idea that
personal experiences with local weather phenomena easily translate into specific perceptions
of climate change and attitudes towards related policies. Political attitudes and public opinions
do not simply reflect personal experience, but are formed in complicated processes entailing
numerous other factors. In some cases, such a disconnect may indicate that people care for the
nation or the globe, instead of their personal conditions. In others that people fail to respond
strongly to clear indications of problems. Building on the idea that public policy should be
somewhat in accordance with public opinion, the not overly strong link between personal
experiences and political attitudes adds another complication to public policy-making under
Supplementary Information The online version contains supplementary material available at https://doi.org/
Acknowledgements We wish to thank the DWD team for their support in obtaining and understanding the
analyzed weather data and Marius Albrecht for excellent research assistance.
Code availability The Stata code for the analyses is available at https://doi.org/10.17605/OSF.IO/8B62S.The
Stata and R codes used to construct the dataset are available on request from the corresponding author.
Climatic Change (2021) 167: 31
Page 16 of20
Author contribution L. Gärtner and H. Schoen collaboratively conceived this paper. L. Gärtner wrote the
initial draft, designed and performed the analyses, and visualized and interpreted the corresponding results. H.
Schoen critically revised and edited previous versions of the manuscript. All authors read and approved the final
Funding Open Access funding enabled and organized by Projekt DEAL. This work was supported by the
German Research Foundation (Grant number SCHO 1358/4-3).
Data availability The data that support the findings of this study are available at https://doi.org/10.17605/OSF.
IO/8B62S. In compliance with the General Data Protection Regulations, the provided data does not include the
personally identifiable information on respondents’postal codes, which was used to match the individual-level
and weather data. However, the raw data is available for analysis at the GESIS –Leibniz-Institut für
Sozialwissenschaften Secure Data Center in Cologne.
Ethical approval Not applicable.
Consent to participate Not applicable.
Consent to publish Not applicable.
Conflict of interest The authors declare competing interests.
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Akerlof K, Maibach EW, Fitzgerald D, Cedeno AY, Neuman A (2013) Do people “personally experience”
global warming, and if so how, and does it matter? Glob Environ Chang 23(1):81–91. https://doi.org/10.
Alló M, Loureiro ML (2014) The role of social norms on preferences towards climate change policies: a meta-
analysis. Energy Policy 73:563–574. https://doi.org/10.1016/j.enpol.2014.04.042
Borick CP, Rabe BG (2014) Weather or not? Examining the impact of meteorological conditions on public
opinion regarding global warming. WCAS 6(3):413–424. https://doi.org/10.1175/WCAS-D-13-00042.1
Brooks J, Oxley D, Vedlitz A, Zahran S, Lindsey C (2014) Abnormal daily temperature and concern about
climate change across the United States. Rev Policy Res 31(3):199–217. https://doi.org/10.1111/ropr.12067
Brulle RJ, Carmichael J, Jenkins JC (2012) Shifting public opinion on climate change: an empirical assessment
of factors influencing concern over climate change in the U.S., 2002–2010. Clim Chang 114(2):169–188.
Capstick SB, Pidgeon NF (2014) Public perception of cold weather events as evidence for and against climate
change. Clim Chang 122(4):695–708. https://doi.org/10.1007/s10584-013-1003-1
Deutsche Welle (2018) After the drought, the storm: German weather chaos continues. DW. https://p.dw.com/p/
32wS9. Last accessed on 08-04-2021
Devine-Wright P, Price J, Leviston Z (2015) My country or my planet? Exploring the influence of multiple place
attachments and ideologicalbeliefs upon climate change attitudes and opinions. Glob Environ Chang 30:68–
Climatic Change (2021) 167: 31 Page 17 of20 31
Duch RM, Stevenson RT (2008) The economic vote: how political and economic institutions condition election
results. Cambridge University Press
DWD (2018) Schadensrückblick des Deutschen Wetterdienstes für die letzten 12 Monate. Deutscher
DWD Climate Data Center (CDC) (2018) Historical daily station observations (temperature, pressure, precipi-
tation, sunshine duration, etc.) for Germany, (Version: V006)
Egan PJ, Mullin M (2012) Turning personal experience into political attitudes: the effect of local weather on
Americans’perceptions about global warming. J Polit 74(3):796–809. https://doi.org/10.1017/
ESS Round 8 (2016) European Social Survey Round 8 Data. Data file edition 2.2. NSD - Norwegian Centre for
Research Data, Norway –Data Archive and Distributor of ESS Data for ESS ERIC. 10.21338/NSD-ESS8-
Eurobarometer (2019) Report: climate change (no. 490; special Eurobarometer). European Commission. https://
ec.europa.eu/clima/sites/clima/files/support/docs/report_2019_en.pdf. Last accessed on 08-04-2021
Evans G, Pickup M (2010) Reversing the causal arrow: the political conditioning of economic perceptions in the
2000–2004 U.S. presidential election cycle. J Polit 72(4):1236–1251. https://doi.org/10.1017/
Fazio RH, Zanna MP (1981) Direct experience and attitude-behavior consistency. In: Berkowitz L (ed) Advances
in experimental social psychology, 14th edn. Academic Press, pp 161–202
Field CB, Barros V, Stocker TF, Dahe Q, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen
SK, Tignor M, Midgley PM (eds) (2012) Managing the risks of extreme events and disasters to advance
climate change adaptation: a special report of working groups I and II of the intergovernmental panel on
climate change. Cambridge University Press, Cambridge
Fielding KS, Hornsey MJ (2016) A social identity analysis of climate change and environmental attitudes and
behaviors: insights and opportunities. Front Psychol 7:121. https://doi.org/10.3389/fpsyg.2016.00121
Filiberto D, Wethington E, Pillemer K, Wells NM, Wysocki M, Parise JT (2009) Older people and climate
change. ASA Generations 33(4):19–25. JSTOR. https://doi.org/10.2307/26555689
Flynn C, Yamasumi E, Fisher S, Snow D, Grant Z, Kirby M, Browning P, Rommerskirchen M, Russell I (2021)
Peoples’climate vote: results. UNDP. https://www.undp.org/sites/g/files/zskgke326/files/publications/
UNDP-Oxford-Peoples-Climate-Vote-Results.pdf. Last accessed on 08-04-2021
Friedrich K, Kaspar F (2019) Rückblick auf das Jahr 2018—Das bisher wärmste Jahr in Deutschland. Deutscher
Wetterdienst - Abteilung Klimaüberwachung
Gilbert DT, Malone PS (1995) The correspondence bias. Psychol Bull 117(1):21–38. https://doi.org/10.1037/
GLES (2019) GLES 2017 pre- and post-election cross section (Cumulation) (Version 3.0.1). GESIS Data
Archive, Cologne. https://doi.org/10.4232/1.13236
Handelsblatt (2019) Waldbrände wüten in Mecklenburg-Vorpommern –Mehrere Ortschaften evakuiert.
DXJunoroobUXbO9j2ZMj-ap6. Last accessed on 08-04-2021
Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Muñoz Sabater J, Nicolas J, Peubey C, Radu R, Rozum
I, Schepers D, Simmons A, Soci C, Dee D, Thépaut J-N (2018) ERA5 hourly data on single levels from
1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.
Hoffarth MR, Hodson G (2016) Green on the outside, red on the inside: erceived environmentalist threat as a
factor explaining political polarization of climate change. J Environ Psychol 45:40–49. https://doi.org/10.
Hornsey MJ, Harris EA, Bain PG, Fielding KS (2016) Meta-analyses of the determinants and outcomes of belief
in climate change. Nat Clim Chang 6(6):622–626. https://doi.org/10.1038/nclimate2943
Howe PD, Leiserowitz A (2013) Who remembers a hot summer or a cold winter? The asymmetric effect of
beliefs about global warming on perceptions of local climate conditions in the U.S. Glob Environ Chang
Howe PD, Boudet H, Leiserowitz A, Maibach EW (2014) Mapping the shadow of experience of extreme
weather events. Clim Chang 127(2):381–389. https://doi.org/10.1007/s10584-014-1253-6
Howe PD, Marlon JR, Mildenberger M, Shield BS (2019) How will climate change shape climate opinion?
Environ Res Lett 14(11):113001. https://doi.org/10.1088/1748-9326/ab466a
Imbery F, Bissolli P, Daßler J, Haeseler S (2019) NeuerRekord der mittlerenJunitemperatur für Deutschland und
intensive Hitzewelle in Europa. Deutscher Wetterdienst - Abteilung Klimaüberwachung. https://www.dwd.
Climatic Change (2021) 167: 31
Page 18 of20
publicationFile&v=1. Last accessed on 08-04-2021
Inglehart R (1970) Public opinion and regional integration. Int Organ 24(4):764–795. Cambridge Core. https://
Jones EE, Harris VA (1967) The attribution of attitudes. J Exp Soc Psychol 3(1):1–24. https://doi.org/10.1016/
Kahneman D, Slovic P, Tversky A (eds) (1982) Judgement under uncertainty: heuristics and biases. Cambridge
University Press. https://doi.org/10.1017/CBO9780511809477
Keller C, Siegrist M, Gutscher H (2006) The role of the affect and availability heuristics in risk communication.
Risk Anal 26(3):631–639. https://doi.org/10.1111/j.1539-6924.2006.00773.x
Kim YM, Garrett K (2012) On-line and memory-based: revisiting the relationship between candidate evaluation
processing models. Polit Behav 34(2):345–368. https://doi.org/10.1007/s11109-011-9158-9
Konisky DM, Hughes L, Kaylor CH (2016) Extreme weather events and climate change concern. Clim Chang
Krosnick JA, Holbrook AL, Lowe L, Visser PS (2006) The origins and consequences of democratic citizens’
policy agendas: a study of popular concern about global warming. Clim Chang 77(1):7–43. https://doi.org/
Kuchler T, Zafar B (2019) Personal experiences and expectations about aggregate outcomes. J Financ 74(5):
Kunda Z (1990) The case for motivated reasoning. Psychol Bull 108(3):480–498. https://doi.org/10.1037/0033-
Lang C (2014) Do weather fluctuations cause people to seek information about climate change? Clim Chang
Li Y, Johnson EJ, Zaval L (2011) Local warming: daily temperature change influences belief in global warming.
Psychol Sci 22(4):454–459. https://doi.org/10.1177/0956797611400913
Lodge M, Taber CS (2013) The rationalizing voter. Cambridge University Press, Cambridge Core. https://doi.
Lodge M, Steenbergen MR, Brau S (1995) The responsive voter: campaign information and the dynamics of
candidate evaluation. Am Polit Sci Rev 89(2):309–326. JSTOR. https://doi.org/10.2307/2082427
Lord CG, Ross L, Lepper MR(1979) Biased assimilation andattitude polarization: the effects of prior theorieson
subsequently considered evidence. J Pers Soc Psychol 37(11):2098–2109
Lujala P, Lein H, Rød JK (2015) Climate change, natural hazards, and risk perception: the role of proximity and
personal experience. Local Environ 20(4):489–509. https://doi.org/10.1080/13549839.2014.887666
Marquart-Pyatt ST, McCright AM, Dietz T, Dunlap RE (2014) Politics eclipses climate extremes for climate
change perceptions. Glob Environ Chang 29:246–257. https://doi.org/10.1016/j.gloenvcha.2014.10.004
McCright AM, Dunlap RE(2011) The politicization of climate change and polarization in the American public’s
views of global warming, 2001–2010. Sociol Q 52(2):155–194. https://doi.org/10.1111/j.1533-8525.2011.
Meinert T, Becker A, Bissolli P, Daßler J, Breidenbach JN, Ziese M (2019) Ursachen und Folgen der
Trockenheit in Deutschland und Europa ab Juni 2019. Deutscher Wetterdienst - Abteilung
trockenheit_juni_juli_2019.pdf?__blob=publicationFile&v=1. Last accessed on 08-04-2021
Myers TA, Maibach EW, Roser-Renouf C, Akerlof K, Leiserowitz AA (2013) The relationship between personal
experience and belief in the reality of global warming. Nat Clim Chang 3(4):343–347. https://doi.org/10.
Nicolai B (2018) In Norddeutschland herrscht die größte Dürre seit 15 Jahren. Welt. https://www.welt.de/
Jahren.html. Last accessed on 08-04-2021
Palm R, Lewis GB, Feng B (2017) What causes people to change their opinion about climate change? Ann Am
Assoc Geogr 107(4):883–896. https://doi.org/10.1080/24694452.2016.1270193
Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification.
Hydrol Earth Syst Sci 11(5):1633–1644. https://doi.org/10.5194/hess-11-1633-2007
Poortinga W, Fisher S,Böhm G, Steg L,Whitmarsh L,Ogunbode C (2018) European attitudes to climate change
and energy: topline results from round 8 of the European social Survey (no. 9; ESS topline results).
European Social Survey ERIC
Reser JP, Bradley GL, Glendon AI, Ellul MC, Callaghan R (2012) Public risk perceptions, understandings, and
responses to climate change and natural disasters in Australia and Great Britain. National Climate Change
Adaptation Research Facility, Gold Cost, Australia
Reser JP, Bradley GL, Ellul MC (2014) Encountering climate change: ‘seeing’is more than ‘believing.’WIREs
Climate Change, 5(4), 521–537. https://doi.org/10.1002/wcc.286
Climatic Change (2021) 167: 31 Page 19 of20 31
Ripberger JT, Jenkins-Smith HC, Silva CL, Carlson DE, Gupta K, Carlson N, Dunlap RE (2017) Bayesian
versus politically motivated reasoning in human perception of climate anomalies. Environ Res Lett 12(11):
Ross L (1977) The intuitive psychologist and his shortcomings: distortions in the attribution process. In:
Berkowitz L (ed) Advances in experimental social psychology, vol 10. Academic Press, pp 173–220.
Roßteutscher S, Schmitt-Beck R, Schoen H, Weßels B, Wolf C, Preißinger M, Kratz A, Wuttke A, Gärtner L
(2018) Short-term campaign panel (GLES 2017) (version 6.0.0). GESIS data archive, Cologne. https://doi.
Rudman LA, McLean MC, Bunzl M (2013) When truth is personally inconvenient, attitudes change: the impact
of extreme weather on implicit support for green politicians and explicit climate-change beliefs. Psychol Sci
Rueter G (2019) Germany among top three countries suffering most from extreme weather events. DW. https://p.
dw.com/p/3UD5A. Last accessed on 08-04-2021
Schmidt-Catran AW, Fairbrother M (2016) The random effects in multilevel models: getting them wrong and
getting them right. Eur Sociol Rev 32(1):23–38. https://doi.org/10.1093/esr/jcv090
Schwirplies C (2018) Citizens’acceptance of climate change adaptation and mitigation: a survey in China,
Germany, and the U.S. Ecol Econ 145:308–322. https://doi.org/10.1016/j.ecolecon.2017.11.003
Shao W, Keim BD, Garand JC, Hamilton LC (2014) Weather, climate, and the economy: explaining risk
perceptions of global warming, 2001–10. WCAS 6(1):119–134. https://doi.org/10.1175/WCAS-D-13-
Simonsohn U, Simmons JP, Nelson LD (2020) Specification curve analysis. Nat Hum Behav 4(11):1208–1214.
Spence A, Poortinga W, Butler C, Pidgeon NF (2011) Perceptions of climate change and willingness to save
energy related to flood experience. Nat Clim Chang 1(1):46–49. https://doi.org/10.1038/nclimate1059
Taylor A, de Bruin WB, Dessai S (2014) Climate change beliefs and perceptions of weather-related changes in
the United Kingdom. Risk Anal 34(11):1995–2004. https://doi.org/10.1111/risa.12234
Tvinnereim E, Fløttum K, Gjerstad Ø, Johannesson MP, Nordø ÅD (2017) Citizens’preferences for tackling
climate change. Quantitative and qualitative analyses of their freely formulated solutions. Glob Environ
Chang 46:34–41. https://doi.org/10.1016/j.gloenvcha.2017.06.005
van der Linden S (2014) On the relationship between personal experience, affect and risk perception: the case of
climate change. Eur J Soc Psychol 44(5):430–440. https://doi.org/10.1002/ejsp.2008
van der Linden S (2015) The social-psychological determinants of climate change risk perceptions: towards a
comprehensive model. J Environ Psychol 41:112–124. https://doi.org/10.1016/j.jenvp.2014.11.012
WAZ (2019) »Dieser Winter war erheblich zu mild«. WAZ. https://interaktiv.waz.de/winter-vergleich-
deutschland/. Last accessed on 08-04-2021
Weber EU (2006) Experience-based and description-based perceptions of long-term risk: why global warming
does not scare us (yet). Clim Chang 77(1):103–120. https://doi.org/10.1007/s10584-006-9060-3
Weber EU (2010) What shapes perceptions of climate change? Wiley Interdiscip Rev Clim Change 1(3):332–
Whitmarsh L (2008) Are flood victims more concerned about climate change than other people? The role of
direct experience in risk perception and behavioural response. Journal of Risk Research 11(3):351–374.
Zeit Online (2018) Drei Dörfer wegen Waldbrand evakuiert. Zeit Online. https://www.zeit.de/gesellschaft/
2Fwww.google.com%2F. Last accessed on 08-04-2021
Ziegler A (2017) Political orientation, environmental values, and climate change beliefs and attitudes: an
empirical cross country analysis. Energy Econ 63:144–153. https://doi.org/10.1016/j.eneco.2017.01.022
Ziese M, Becker A, Finger P, Meyer-Christoffer A, RudolfB, Schneider U (2013) GPCC Drought Index Product
(GPCC_DI) at 1.0°. Deutscher Wetterdienst. 10.5676/DWD_GPCC/DI_M_100
Ziese M, Schneider U, Meyer-Christoffer A, Schamm K, Vido J, Finger P, Bissolli P, Pietzsch S, Becker A
(2014) The GPCC drought index –a new, combined and gridded global drought index. Earth System
Science Data 6(2):285–295. https://doi.org/10.5194/essd-6-285-2014
Publisher’snote Springer Nature remains neutral with regard to jurisdictional claims in published maps
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Climatic Change (2021) 167: 31
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